NBA analytics: Going data pro
For the NBA, like every other sports league, awards are important. They can generate attention, spur debate, make money, and involve fans, players, and experts, among others. Is there data science and analytics behind them — can there or should there be? We picked the NBA Most Improved Player award as an example to analyze some aspects of data-driven culture.
Read More →Kafka: The story so far
Hard problems at scale, the future of application development, and building an open source business. If any of that is of interest, or if you want to know about Kafka, real-time data, and streaming APIs in the cloud and beyond, Jay Kreps has some thoughts to share.
Read More →Automating automation: a framework for developing and marketing deep learning models
Are you sold on the benefits of adding automation to your stack, but put off by the high entry barrier to this game? The NeoPulse Framework promises to ease the burden of developing Deep Learning models by introducing a number of interesting concepts.
Read More →Spark gets automation: Analyzing code and tuning clusters in production
Spark is the hottest big data tool around, and most Hadoop users are moving towards using it in production. Problem is, programming and tuning Spark is hard. But Pepperdata and Alpine Data bring solutions to lighten the load.
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